From Conversations To Mechanisms: Aligning Advertiser Incentives In AI-Powered Product Recommendations

We study the design of efficient dynamic recommendation systems, such as AI shopping assistants, in which a platform interacts with a user over multiple rounds to identify the most suitable product among those offered by advertisers. Advertisers have multi-dimensional private information: their private value from a purchase and private information about the user’s preferences. In each round, the platform displays recommendations; the user learns product characteristics of the shown items and then chooses whether to purchase, exit without purchasing, or submit a new query. These actions generate a stream of feedback—purchase, exit, and follow-up queries—that is informative about the user’s preferences and can be used both to refine future recommendations and to design contingent transfers. We introduce a class of data-driven dynamic team mechanisms that condition payments on realized user feedback. Our main result shows that data-driven dynamic team mechanisms achieve periodic ex-post implementation of the efficient allocation rule. We then develop variants that guarantee participation and deliver budget surplus, and provide conditions under which these properties can be jointly attained.